Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "159"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 159 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 159, Node N13:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460009 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.402337 28.058208 -1.157088 -0.555641 -0.370389 2.058987 -0.291886 0.565805 0.5725 0.4687 0.3243 nan nan
2460008 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.840760 35.105396 -1.623795 -0.630179 -0.805760 2.006933 -0.720644 0.434725 0.6148 0.5292 0.2869 nan nan
2460007 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.640833 24.973713 -1.171098 -0.580437 -1.007901 2.574406 -0.107475 2.203997 0.5789 0.4993 0.3180 nan nan
2459999 RF_maintenance 0.00% 98.91% 99.16% 0.00% - - nan nan nan nan nan nan nan nan 0.2629 0.2663 0.2062 nan nan
2459998 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.381368 24.558610 -1.060495 -0.429527 -0.838076 2.041503 -0.530124 0.129768 0.5709 0.4604 0.3403 nan nan
2459997 RF_maintenance 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459996 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.569638 27.208682 -0.856980 -0.398776 -0.929894 2.187918 0.028276 1.847463 0.5961 0.4849 0.3519 nan nan
2459995 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.519726 28.420830 -1.458906 -0.636073 -0.369346 3.234198 0.161502 23.223973 0.5754 0.4817 0.3503 nan nan
2459994 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.405009 25.488056 -1.316010 -0.594154 -0.315255 4.734707 0.952347 19.301957 0.5683 0.4905 0.3522 nan nan
2459993 RF_maintenance 100.00% 100.00% 100.00% 0.00% - - 274.517486 274.599023 inf inf 3669.079748 3670.351297 6753.038038 6733.078425 nan nan nan nan nan
2459991 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.697103 23.482901 -1.342536 -0.778508 -0.089585 5.794077 -0.461058 42.586377 0.5842 0.5306 0.3585 nan nan
2459990 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.627427 11.690909 -1.319678 -1.009496 -0.310933 5.263809 -0.479633 21.973858 0.5803 0.5655 0.3635 nan nan
2459989 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.413064 2.929442 -1.005720 -1.034373 -0.218721 0.977811 -0.290945 12.443759 0.5737 0.5916 0.3704 nan nan
2459988 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.519311 12.161177 -1.450684 -1.156543 0.149521 4.409362 -0.336361 77.538776 0.5773 0.5689 0.3512 nan nan
2459987 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.424738 27.335078 -1.383706 -0.571282 -0.832162 2.832640 -0.150900 6.424853 0.5870 0.4992 0.3405 nan nan
2459986 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.707015 29.452730 -1.492274 -0.716382 -0.385559 5.610013 -0.074228 20.203353 0.6049 0.5409 0.3115 nan nan
2459985 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.358450 31.408510 -1.373274 -0.579426 -0.664261 3.612942 -0.534838 35.366233 0.5870 0.4838 0.3494 nan nan
2459984 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.565746 26.432240 -1.323198 -0.656373 -1.259579 3.814309 -0.655422 0.027284 0.6020 0.5256 0.3332 nan nan
2459983 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.412718 27.084041 -1.392037 -0.626627 -0.286196 6.315442 -0.329396 20.044162 0.6167 0.5528 0.2889 nan nan
2459982 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 1.096404 17.651612 -1.032450 -0.650539 -1.113392 2.584517 -0.961396 2.948816 0.6604 0.6006 0.2797 nan nan
2459981 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.417341 24.925296 -1.485741 -0.713067 0.295525 6.212868 -0.478876 66.714918 0.5838 0.5040 0.3478 nan nan
2459980 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.341164 26.543002 -1.553205 -0.750840 -0.830844 5.099357 -0.762942 3.326785 0.6276 0.5372 0.2868 nan nan
2459979 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.424194 20.755041 -1.440371 -0.943238 0.028616 5.092987 -0.762241 43.558260 0.5764 0.5292 0.3524 nan nan
2459978 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.386365 26.138935 -1.443254 -0.789886 0.096382 2.954087 -0.831292 41.511885 0.5773 0.5015 0.3549 nan nan
2459977 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.394564 26.992591 -1.492316 -0.716329 -0.503915 6.860818 -0.756598 25.708444 0.5432 0.4591 0.3126 nan nan
2459976 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 0.450124 27.139377 -1.573869 -0.768763 -0.185379 4.191466 -0.454397 32.748403 0.5815 0.4973 0.3465 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 159: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 28.058208 0.402337 28.058208 -1.157088 -0.555641 -0.370389 2.058987 -0.291886 0.565805

Antenna 159: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 35.105396 35.105396 0.840760 -0.630179 -1.623795 2.006933 -0.805760 0.434725 -0.720644

Antenna 159: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 24.973713 0.640833 24.973713 -1.171098 -0.580437 -1.007901 2.574406 -0.107475 2.203997

Antenna 159: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape nan nan nan nan nan nan nan nan nan

Antenna 159: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 24.558610 0.381368 24.558610 -1.060495 -0.429527 -0.838076 2.041503 -0.530124 0.129768

Antenna 159: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance ee Shape nan nan nan inf inf nan nan nan nan

Antenna 159: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 27.208682 0.569638 27.208682 -0.856980 -0.398776 -0.929894 2.187918 0.028276 1.847463

Antenna 159: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 28.420830 0.519726 28.420830 -1.458906 -0.636073 -0.369346 3.234198 0.161502 23.223973

Antenna 159: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 25.488056 0.405009 25.488056 -1.316010 -0.594154 -0.315255 4.734707 0.952347 19.301957

Antenna 159: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance ee Power inf 274.517486 274.599023 inf inf 3669.079748 3670.351297 6753.038038 6733.078425

Antenna 159: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 42.586377 0.697103 23.482901 -1.342536 -0.778508 -0.089585 5.794077 -0.461058 42.586377

Antenna 159: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 21.973858 11.690909 0.627427 -1.009496 -1.319678 5.263809 -0.310933 21.973858 -0.479633

Antenna 159: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 12.443759 2.929442 0.413064 -1.034373 -1.005720 0.977811 -0.218721 12.443759 -0.290945

Antenna 159: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 77.538776 12.161177 0.519311 -1.156543 -1.450684 4.409362 0.149521 77.538776 -0.336361

Antenna 159: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 27.335078 0.424738 27.335078 -1.383706 -0.571282 -0.832162 2.832640 -0.150900 6.424853

Antenna 159: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 29.452730 29.452730 0.707015 -0.716382 -1.492274 5.610013 -0.385559 20.203353 -0.074228

Antenna 159: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 35.366233 31.408510 0.358450 -0.579426 -1.373274 3.612942 -0.664261 35.366233 -0.534838

Antenna 159: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 26.432240 0.565746 26.432240 -1.323198 -0.656373 -1.259579 3.814309 -0.655422 0.027284

Antenna 159: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 27.084041 0.412718 27.084041 -1.392037 -0.626627 -0.286196 6.315442 -0.329396 20.044162

Antenna 159: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 17.651612 1.096404 17.651612 -1.032450 -0.650539 -1.113392 2.584517 -0.961396 2.948816

Antenna 159: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 66.714918 24.925296 0.417341 -0.713067 -1.485741 6.212868 0.295525 66.714918 -0.478876

Antenna 159: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 26.543002 26.543002 0.341164 -0.750840 -1.553205 5.099357 -0.830844 3.326785 -0.762942

Antenna 159: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 43.558260 0.424194 20.755041 -1.440371 -0.943238 0.028616 5.092987 -0.762241 43.558260

Antenna 159: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 41.511885 26.138935 0.386365 -0.789886 -1.443254 2.954087 0.096382 41.511885 -0.831292

Antenna 159: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Shape 26.992591 0.394564 26.992591 -1.492316 -0.716329 -0.503915 6.860818 -0.756598 25.708444

Antenna 159: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
159 N13 RF_maintenance nn Temporal Discontinuties 32.748403 27.139377 0.450124 -0.768763 -1.573869 4.191466 -0.185379 32.748403 -0.454397
In [ ]: